Overview

Dataset statistics

Number of variables32
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory500.1 KiB
Average record size in memory256.1 B

Variable types

Numeric14
Categorical17
Boolean1

Alerts

foreign_worker is highly imbalanced (76.7%)Imbalance
other_parties is highly imbalanced (66.6%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
feat01 has unique valuesUnique
feat02 has unique valuesUnique
feat03 has unique valuesUnique
feat04 has unique valuesUnique
feat05 has unique valuesUnique
feat06 has unique valuesUnique
feat07 has unique valuesUnique
feat08 has unique valuesUnique
feat09 has unique valuesUnique
feat10 has unique valuesUnique

Reproduction

Analysis started2024-01-16 21:56:37.171010
Analysis finished2024-01-16 21:56:47.800816
Duration10.63 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.5
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:47.851332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100.95
Q1500.75
median1000.5
Q31500.25
95-th percentile1900.05
Maximum2000
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.49459
Coefficient of variation (CV)0.57720599
Kurtosis-1.2
Mean1000.5
Median Absolute Deviation (MAD)500
Skewness0
Sum2001000
Variance333500
MonotonicityStrictly increasing
2024-01-16T22:56:47.920336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1330 1
 
0.1%
1343 1
 
0.1%
1342 1
 
0.1%
1341 1
 
0.1%
1340 1
 
0.1%
1339 1
 
0.1%
1338 1
 
0.1%
1337 1
 
0.1%
1336 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%

age
Real number (ℝ)

Distinct53
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.326
Minimum19
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:47.986846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q127
median33
Q342
95-th percentile59
Maximum75
Range56
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.232756
Coefficient of variation (CV)0.31797419
Kurtosis0.64411663
Mean35.326
Median Absolute Deviation (MAD)7
Skewness1.0351368
Sum70652
Variance126.17481
MonotonicityNot monotonic
2024-01-16T22:56:48.047360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 110
 
5.5%
26 100
 
5.0%
23 95
 
4.8%
24 94
 
4.7%
31 88
 
4.4%
28 81
 
4.0%
36 80
 
4.0%
32 77
 
3.9%
30 76
 
3.8%
29 76
 
3.8%
Other values (43) 1123
56.1%
ValueCountFrequency (%)
19 5
 
0.2%
20 30
 
1.5%
21 28
 
1.4%
22 55
2.8%
23 95
4.8%
24 94
4.7%
25 74
3.7%
26 100
5.0%
27 110
5.5%
28 81
4.0%
ValueCountFrequency (%)
75 3
 
0.1%
74 7
0.4%
70 3
 
0.1%
68 4
 
0.2%
67 8
0.4%
66 8
0.4%
65 10
0.5%
64 11
0.5%
63 14
0.7%
62 6
0.3%

checking_status
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
'no checking'
765 
'<0'
584 
'0<=X<200'
525 
'>=200'
126 

Length

Max length13
Median length10
Mean length9.2065
Min length4

Characters and Unicode

Total characters18413
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'no checking'
2nd row'no checking'
3rd row'no checking'
4th row'no checking'
5th row'no checking'

Common Values

ValueCountFrequency (%)
'no checking' 765
38.2%
'<0' 584
29.2%
'0<=X<200' 525
26.2%
'>=200' 126
 
6.3%

Length

2024-01-16T22:56:48.105360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:48.155871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
no 765
27.7%
checking 765
27.7%
0 584
21.1%
0<=x<200 525
19.0%
200 126
 
4.6%

Most occurring characters

ValueCountFrequency (%)
' 4000
21.7%
0 2411
13.1%
< 1634
8.9%
n 1530
 
8.3%
c 1530
 
8.3%
o 765
 
4.2%
765
 
4.2%
h 765
 
4.2%
e 765
 
4.2%
k 765
 
4.2%
Other values (6) 3483
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7650
41.5%
Other Punctuation 4000
21.7%
Decimal Number 3062
16.6%
Math Symbol 2411
 
13.1%
Space Separator 765
 
4.2%
Uppercase Letter 525
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1530
20.0%
c 1530
20.0%
o 765
10.0%
h 765
10.0%
e 765
10.0%
k 765
10.0%
i 765
10.0%
g 765
10.0%
Math Symbol
ValueCountFrequency (%)
< 1634
67.8%
= 651
 
27.0%
> 126
 
5.2%
Decimal Number
ValueCountFrequency (%)
0 2411
78.7%
2 651
 
21.3%
Other Punctuation
ValueCountFrequency (%)
' 4000
100.0%
Space Separator
ValueCountFrequency (%)
765
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10238
55.6%
Latin 8175
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1530
18.7%
c 1530
18.7%
o 765
9.4%
h 765
9.4%
e 765
9.4%
k 765
9.4%
i 765
9.4%
g 765
9.4%
X 525
 
6.4%
Common
ValueCountFrequency (%)
' 4000
39.1%
0 2411
23.5%
< 1634
16.0%
765
 
7.5%
= 651
 
6.4%
2 651
 
6.4%
> 126
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 4000
21.7%
0 2411
13.1%
< 1634
8.9%
n 1530
 
8.3%
c 1530
 
8.3%
o 765
 
4.2%
765
 
4.2%
h 765
 
4.2%
e 765
 
4.2%
k 765
 
4.2%
Other values (6) 3483
18.9%

class
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
good
1382 
bad
618 

Length

Max length4
Median length4
Mean length3.691
Min length3

Characters and Unicode

Total characters7382
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowgood
4th rowgood
5th rowgood

Common Values

ValueCountFrequency (%)
good 1382
69.1%
bad 618
30.9%

Length

2024-01-16T22:56:48.214875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:48.265382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
good 1382
69.1%
bad 618
30.9%

Most occurring characters

ValueCountFrequency (%)
o 2764
37.4%
d 2000
27.1%
g 1382
18.7%
b 618
 
8.4%
a 618
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7382
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2764
37.4%
d 2000
27.1%
g 1382
18.7%
b 618
 
8.4%
a 618
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7382
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2764
37.4%
d 2000
27.1%
g 1382
18.7%
b 618
 
8.4%
a 618
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2764
37.4%
d 2000
27.1%
g 1382
18.7%
b 618
 
8.4%
a 618
 
8.4%

credit_amount
Real number (ℝ)

Distinct1684
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3238.904
Minimum250
Maximum18412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:48.320387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile641.95
Q11370.5
median2258
Q33990.75
95-th percentile9066.25
Maximum18412
Range18162
Interquartile range (IQR)2620.25

Descriptive statistics

Standard deviation2809.6835
Coefficient of variation (CV)0.86747971
Kurtosis3.9635044
Mean3238.904
Median Absolute Deviation (MAD)1102
Skewness1.8857482
Sum6477808
Variance7894321.5
MonotonicityNot monotonic
2024-01-16T22:56:48.387578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 15
 
0.8%
440 4
 
0.2%
1082 4
 
0.2%
1981 4
 
0.2%
1425 4
 
0.2%
1289 4
 
0.2%
2580 3
 
0.1%
2618 3
 
0.1%
2110 3
 
0.1%
784 3
 
0.1%
Other values (1674) 1953
97.7%
ValueCountFrequency (%)
250 15
0.8%
255 1
 
0.1%
266 1
 
0.1%
271 1
 
0.1%
280 1
 
0.1%
281 1
 
0.1%
291 1
 
0.1%
307 1
 
0.1%
314 1
 
0.1%
317 1
 
0.1%
ValueCountFrequency (%)
18412 1
0.1%
18257 1
0.1%
16316 1
0.1%
15946 1
0.1%
15854 1
0.1%
15824 1
0.1%
15547 1
0.1%
15397 1
0.1%
14891 1
0.1%
14819 1
0.1%

credit_history
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
'existing paid'
1056 
'critical/other existing credit'
593 
'delayed previously'
176 
'all paid'
 
99
'no credits/all paid'
 
76

Length

Max length32
Median length15
Mean length20.461
Min length10

Characters and Unicode

Total characters40922
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'existing paid'
2nd row'delayed previously'
3rd row'critical/other existing credit'
4th row'delayed previously'
5th row'existing paid'

Common Values

ValueCountFrequency (%)
'existing paid' 1056
52.8%
'critical/other existing credit' 593
29.6%
'delayed previously' 176
 
8.8%
'all paid' 99
 
5.0%
'no credits/all paid' 76
 
3.8%

Length

2024-01-16T22:56:48.445615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:48.496613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
existing 1649
35.3%
paid 1231
26.4%
critical/other 593
 
12.7%
credit 593
 
12.7%
delayed 176
 
3.8%
previously 176
 
3.8%
all 99
 
2.1%
no 76
 
1.6%
credits/all 76
 
1.6%

Most occurring characters

ValueCountFrequency (%)
i 6560
16.0%
' 4000
 
9.8%
t 3504
 
8.6%
e 3439
 
8.4%
2669
 
6.5%
d 2252
 
5.5%
a 2175
 
5.3%
r 2031
 
5.0%
s 1901
 
4.6%
c 1855
 
4.5%
Other values (11) 10536
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33584
82.1%
Other Punctuation 4669
 
11.4%
Space Separator 2669
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 6560
19.5%
t 3504
10.4%
e 3439
10.2%
d 2252
 
6.7%
a 2175
 
6.5%
r 2031
 
6.0%
s 1901
 
5.7%
c 1855
 
5.5%
n 1725
 
5.1%
g 1649
 
4.9%
Other values (8) 6493
19.3%
Other Punctuation
ValueCountFrequency (%)
' 4000
85.7%
/ 669
 
14.3%
Space Separator
ValueCountFrequency (%)
2669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33584
82.1%
Common 7338
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 6560
19.5%
t 3504
10.4%
e 3439
10.2%
d 2252
 
6.7%
a 2175
 
6.5%
r 2031
 
6.0%
s 1901
 
5.7%
c 1855
 
5.5%
n 1725
 
5.1%
g 1649
 
4.9%
Other values (8) 6493
19.3%
Common
ValueCountFrequency (%)
' 4000
54.5%
2669
36.4%
/ 669
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 6560
16.0%
' 4000
 
9.8%
t 3504
 
8.6%
e 3439
 
8.4%
2669
 
6.5%
d 2252
 
5.5%
a 2175
 
5.3%
r 2031
 
5.0%
s 1901
 
4.6%
c 1855
 
4.5%
Other values (11) 10536
25.7%

duration
Real number (ℝ)

Distinct33
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.7055
Minimum4
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:48.551651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q112
median18
Q324
95-th percentile48
Maximum72
Range68
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.960531
Coefficient of variation (CV)0.57764994
Kurtosis0.94822467
Mean20.7055
Median Absolute Deviation (MAD)6
Skewness1.0996627
Sum41411
Variance143.0543
MonotonicityNot monotonic
2024-01-16T22:56:48.603648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
24 366
18.3%
12 362
18.1%
18 226
11.3%
36 166
8.3%
6 154
7.7%
15 128
 
6.4%
9 100
 
5.0%
48 97
 
4.9%
30 79
 
4.0%
21 60
 
3.0%
Other values (23) 262
13.1%
ValueCountFrequency (%)
4 14
 
0.7%
5 2
 
0.1%
6 154
7.7%
7 7
 
0.4%
8 15
 
0.8%
9 100
 
5.0%
10 58
 
2.9%
11 20
 
1.0%
12 362
18.1%
13 9
 
0.4%
ValueCountFrequency (%)
72 2
 
0.1%
60 24
 
1.2%
54 3
 
0.1%
48 97
4.9%
47 1
 
0.1%
45 7
 
0.4%
42 22
 
1.1%
40 3
 
0.1%
39 6
 
0.3%
36 166
8.3%

employment
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
'1<=X<4'
694 
'>=7'
478 
'4<=X<7'
365 
'<1'
342 
unemployed
121 

Length

Max length10
Median length8
Mean length6.72
Min length4

Characters and Unicode

Total characters13440
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'>=7'
2nd row'4<=X<7'
3rd row'>=7'
4th row'1<=X<4'
5th row'4<=X<7'

Common Values

ValueCountFrequency (%)
'1<=X<4' 694
34.7%
'>=7' 478
23.9%
'4<=X<7' 365
18.2%
'<1' 342
17.1%
unemployed 121
 
6.0%

Length

2024-01-16T22:56:48.681696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:48.736019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1<=x<4 694
34.7%
7 478
23.9%
4<=x<7 365
18.2%
1 342
17.1%
unemployed 121
 
6.0%

Most occurring characters

ValueCountFrequency (%)
' 3758
28.0%
< 2460
18.3%
= 1537
11.4%
X 1059
 
7.9%
4 1059
 
7.9%
1 1036
 
7.7%
7 843
 
6.3%
> 478
 
3.6%
e 242
 
1.8%
l 121
 
0.9%
Other values (7) 847
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Math Symbol 4475
33.3%
Other Punctuation 3758
28.0%
Decimal Number 2938
21.9%
Lowercase Letter 1210
 
9.0%
Uppercase Letter 1059
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 242
20.0%
l 121
10.0%
y 121
10.0%
o 121
10.0%
u 121
10.0%
p 121
10.0%
m 121
10.0%
n 121
10.0%
d 121
10.0%
Math Symbol
ValueCountFrequency (%)
< 2460
55.0%
= 1537
34.3%
> 478
 
10.7%
Decimal Number
ValueCountFrequency (%)
4 1059
36.0%
1 1036
35.3%
7 843
28.7%
Other Punctuation
ValueCountFrequency (%)
' 3758
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1059
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11171
83.1%
Latin 2269
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 1059
46.7%
e 242
 
10.7%
l 121
 
5.3%
y 121
 
5.3%
o 121
 
5.3%
u 121
 
5.3%
p 121
 
5.3%
m 121
 
5.3%
n 121
 
5.3%
d 121
 
5.3%
Common
ValueCountFrequency (%)
' 3758
33.6%
< 2460
22.0%
= 1537
13.8%
4 1059
 
9.5%
1 1036
 
9.3%
7 843
 
7.5%
> 478
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 3758
28.0%
< 2460
18.3%
= 1537
11.4%
X 1059
 
7.9%
4 1059
 
7.9%
1 1036
 
7.7%
7 843
 
6.3%
> 478
 
3.6%
e 242
 
1.8%
l 121
 
0.9%
Other values (7) 847
 
6.3%

existing_credits
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1253 
2
672 
3
 
63
4
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

Length

2024-01-16T22:56:48.793021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:48.840566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1253
62.6%
2 672
33.6%
3 63
 
3.1%
4 12
 
0.6%

feat01
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46380843
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:48.902565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21958448
Q10.35930148
median0.46202594
Q30.56625547
95-th percentile0.71389551
Maximum1
Range1
Interquartile range (IQR)0.20695399

Descriptive statistics

Standard deviation0.15196741
Coefficient of variation (CV)0.32765125
Kurtosis-0.11673989
Mean0.46380843
Median Absolute Deviation (MAD)0.10361271
Skewness0.10866473
Sum927.61686
Variance0.023094095
MonotonicityNot monotonic
2024-01-16T22:56:48.975599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2398984278 1
 
0.1%
0.2953242772 1
 
0.1%
0.4860486759 1
 
0.1%
0.45615754 1
 
0.1%
0.5996993404 1
 
0.1%
0.4503090515 1
 
0.1%
0.3256915759 1
 
0.1%
0.4998882231 1
 
0.1%
0.4116441579 1
 
0.1%
0.7284594134 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0 1
0.1%
0.01303784679 1
0.1%
0.02577123511 1
0.1%
0.03571668804 1
0.1%
0.04762137859 1
0.1%
0.06034892084 1
0.1%
0.08160078391 1
0.1%
0.0912368217 1
0.1%
0.09212543608 1
0.1%
0.09386725769 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.9475428754 1
0.1%
0.9255986779 1
0.1%
0.9123538945 1
0.1%
0.9032285253 1
0.1%
0.8929741526 1
0.1%
0.8817804143 1
0.1%
0.8813787542 1
0.1%
0.8690458544 1
0.1%
0.8588361041 1
0.1%

feat02
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57660683
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.049115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.37670584
Q10.50845687
median0.57965906
Q30.65324003
95-th percentile0.76570688
Maximum1
Range1
Interquartile range (IQR)0.14478316

Descriptive statistics

Standard deviation0.11629093
Coefficient of variation (CV)0.20168149
Kurtosis0.88073334
Mean0.57660683
Median Absolute Deviation (MAD)0.0724858
Skewness-0.31785322
Sum1153.2137
Variance0.013523579
MonotonicityNot monotonic
2024-01-16T22:56:49.126633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6142302019 1
 
0.1%
0.5617704607 1
 
0.1%
0.915239038 1
 
0.1%
0.4846403118 1
 
0.1%
0.3978838968 1
 
0.1%
0.6175251976 1
 
0.1%
0.5379019504 1
 
0.1%
0.3557253858 1
 
0.1%
0.7182678736 1
 
0.1%
0.4505633814 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0 1
0.1%
0.07065612562 1
0.1%
0.128671487 1
0.1%
0.1354354399 1
0.1%
0.1391260955 1
0.1%
0.1456761813 1
0.1%
0.1657218849 1
0.1%
0.1808587005 1
0.1%
0.214097843 1
0.1%
0.2191159137 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.9803092994 1
0.1%
0.9225789963 1
0.1%
0.915239038 1
0.1%
0.8983802866 1
0.1%
0.8897000511 1
0.1%
0.8857042666 1
0.1%
0.8731498265 1
0.1%
0.8674734822 1
0.1%
0.8612673628 1
0.1%

feat03
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0362634
Minimum0.092220848
Maximum1.8410465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.343653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.092220848
5-th percentile0.50337588
Q10.78537352
median1.0401109
Q31.2881796
95-th percentile1.5517694
Maximum1.8410465
Range1.7488257
Interquartile range (IQR)0.50280608

Descriptive statistics

Standard deviation0.32766316
Coefficient of variation (CV)0.3161968
Kurtosis-0.65495837
Mean1.0362634
Median Absolute Deviation (MAD)0.25047039
Skewness-0.081636236
Sum2072.5267
Variance0.10736315
MonotonicityNot monotonic
2024-01-16T22:56:49.403654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.309658621 1
 
0.1%
0.9141307935 1
 
0.1%
0.6024412314 1
 
0.1%
0.8433159892 1
 
0.1%
0.750806052 1
 
0.1%
1.064716429 1
 
0.1%
1.46552358 1
 
0.1%
0.9713447508 1
 
0.1%
1.161073356 1
 
0.1%
1.49815327 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.09222084757 1
0.1%
0.1102000459 1
0.1%
0.1195705855 1
0.1%
0.2308731058 1
0.1%
0.2352024594 1
0.1%
0.2421304017 1
0.1%
0.2481220301 1
0.1%
0.2488679834 1
0.1%
0.2498380404 1
0.1%
0.2501291486 1
0.1%
ValueCountFrequency (%)
1.84104654 1
0.1%
1.820551743 1
0.1%
1.81863373 1
0.1%
1.786289997 1
0.1%
1.776601662 1
0.1%
1.768208552 1
0.1%
1.766911381 1
0.1%
1.76632784 1
0.1%
1.757625422 1
0.1%
1.749985058 1
0.1%

feat04
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98356392
Minimum0.12481399
Maximum1.8776639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.463161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.12481399
5-th percentile0.46846648
Q10.73626214
median0.99070768
Q31.2314109
95-th percentile1.487849
Maximum1.8776639
Range1.75285
Interquartile range (IQR)0.49514879

Descriptive statistics

Standard deviation0.32138874
Coefficient of variation (CV)0.32675938
Kurtosis-0.78185124
Mean0.98356392
Median Absolute Deviation (MAD)0.25019848
Skewness-0.055675667
Sum1967.1278
Variance0.10329072
MonotonicityNot monotonic
2024-01-16T22:56:49.523164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9764457258 1
 
0.1%
1.437547263 1
 
0.1%
1.318420991 1
 
0.1%
1.018432569 1
 
0.1%
1.326229483 1
 
0.1%
1.099438935 1
 
0.1%
1.19238331 1
 
0.1%
0.7946874808 1
 
0.1%
0.6352639852 1
 
0.1%
0.9948328081 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.1248139871 1
0.1%
0.1463171125 1
0.1%
0.1897647828 1
0.1%
0.2006078398 1
0.1%
0.2010651066 1
0.1%
0.2367038524 1
0.1%
0.2460220052 1
0.1%
0.2473846713 1
0.1%
0.2607777121 1
0.1%
0.2613713767 1
0.1%
ValueCountFrequency (%)
1.877663941 1
0.1%
1.799240539 1
0.1%
1.784405638 1
0.1%
1.748792312 1
0.1%
1.740584316 1
0.1%
1.682527802 1
0.1%
1.671119286 1
0.1%
1.669082043 1
0.1%
1.668377219 1
0.1%
1.652761714 1
0.1%

feat05
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98682419
Minimum0.081459654
Maximum1.8781879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.583670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.081459654
5-th percentile0.46174148
Q10.72837858
median0.97899226
Q31.242919
95-th percentile1.5351376
Maximum1.8781879
Range1.7967283
Interquartile range (IQR)0.5145404

Descriptive statistics

Standard deviation0.33473211
Coefficient of variation (CV)0.33920136
Kurtosis-0.70739714
Mean0.98682419
Median Absolute Deviation (MAD)0.25618023
Skewness0.010293335
Sum1973.6484
Variance0.11204558
MonotonicityNot monotonic
2024-01-16T22:56:49.647706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7099717159 1
 
0.1%
1.093334768 1
 
0.1%
0.9654023844 1
 
0.1%
0.6701074655 1
 
0.1%
1.544046191 1
 
0.1%
0.6893069911 1
 
0.1%
1.07426875 1
 
0.1%
0.931735424 1
 
0.1%
0.4810747262 1
 
0.1%
0.8449231847 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.08145965407 1
0.1%
0.1037076624 1
0.1%
0.1058619264 1
0.1%
0.1246123251 1
0.1%
0.1338336509 1
0.1%
0.1755747317 1
0.1%
0.1761897585 1
0.1%
0.1835692315 1
0.1%
0.1985862508 1
0.1%
0.2141448008 1
0.1%
ValueCountFrequency (%)
1.878187934 1
0.1%
1.873369965 1
0.1%
1.773040175 1
0.1%
1.765111439 1
0.1%
1.760482142 1
0.1%
1.758384959 1
0.1%
1.758084732 1
0.1%
1.755375761 1
0.1%
1.750100523 1
0.1%
1.747165182 1
0.1%

feat06
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0169259
Minimum0.16234566
Maximum1.8433808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.709706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.16234566
5-th percentile0.49527726
Q10.76799539
median1.0247149
Q31.263423
95-th percentile1.5220678
Maximum1.8433808
Range1.6810351
Interquartile range (IQR)0.49542765

Descriptive statistics

Standard deviation0.32049382
Coefficient of variation (CV)0.31515947
Kurtosis-0.74669601
Mean1.0169259
Median Absolute Deviation (MAD)0.24957399
Skewness-0.043081085
Sum2033.8518
Variance0.10271629
MonotonicityNot monotonic
2024-01-16T22:56:49.768858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5697096285 1
 
0.1%
1.323234778 1
 
0.1%
1.249414149 1
 
0.1%
0.9147314151 1
 
0.1%
0.9503955334 1
 
0.1%
1.272661217 1
 
0.1%
1.31528423 1
 
0.1%
1.357362696 1
 
0.1%
1.162946247 1
 
0.1%
1.229982419 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.162345661 1
0.1%
0.1775384731 1
0.1%
0.2080852952 1
0.1%
0.2175219811 1
0.1%
0.2344850641 1
0.1%
0.2559564259 1
0.1%
0.2587082471 1
0.1%
0.2608127729 1
0.1%
0.2619666619 1
0.1%
0.2696971823 1
0.1%
ValueCountFrequency (%)
1.843380806 1
0.1%
1.79723531 1
0.1%
1.781262729 1
0.1%
1.767118688 1
0.1%
1.764654933 1
0.1%
1.750193221 1
0.1%
1.747027309 1
0.1%
1.739690189 1
0.1%
1.723407031 1
0.1%
1.701963076 1
0.1%

feat07
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97558017
Minimum0.10901861
Maximum1.8088549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.827074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.10901861
5-th percentile0.4501102
Q10.71586471
median0.97861019
Q31.2337301
95-th percentile1.4930485
Maximum1.8088549
Range1.6998363
Interquartile range (IQR)0.51786535

Descriptive statistics

Standard deviation0.32907026
Coefficient of variation (CV)0.33730724
Kurtosis-0.75460802
Mean0.97558017
Median Absolute Deviation (MAD)0.25808476
Skewness0.001021948
Sum1951.1603
Variance0.10828723
MonotonicityNot monotonic
2024-01-16T22:56:49.886453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9508351096 1
 
0.1%
0.7390934747 1
 
0.1%
1.718746312 1
 
0.1%
1.045999774 1
 
0.1%
1.030333539 1
 
0.1%
1.187348207 1
 
0.1%
0.9535704999 1
 
0.1%
1.630964703 1
 
0.1%
0.9068550023 1
 
0.1%
0.6225975465 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.1090186149 1
0.1%
0.1406046886 1
0.1%
0.1865862602 1
0.1%
0.2060372116 1
0.1%
0.2166989075 1
0.1%
0.2198584608 1
0.1%
0.2287012131 1
0.1%
0.2401029731 1
0.1%
0.2426106374 1
0.1%
0.2497997504 1
0.1%
ValueCountFrequency (%)
1.808854866 1
0.1%
1.796121509 1
0.1%
1.788255529 1
0.1%
1.764542335 1
0.1%
1.761704114 1
0.1%
1.747396812 1
0.1%
1.740450025 1
0.1%
1.734727301 1
0.1%
1.733844591 1
0.1%
1.73042806 1
0.1%

feat08
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0148103
Minimum0.1014964
Maximum1.8176515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:49.944990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.1014964
5-th percentile0.5004645
Q10.76173397
median1.0161181
Q31.2605098
95-th percentile1.5323617
Maximum1.8176515
Range1.716155
Interquartile range (IQR)0.49877581

Descriptive statistics

Standard deviation0.32048267
Coefficient of variation (CV)0.3158055
Kurtosis-0.73665607
Mean1.0148103
Median Absolute Deviation (MAD)0.25039096
Skewness-0.00020516267
Sum2029.6206
Variance0.10270914
MonotonicityNot monotonic
2024-01-16T22:56:50.003990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5814581661 1
 
0.1%
1.005689318 1
 
0.1%
1.106400368 1
 
0.1%
1.351283482 1
 
0.1%
1.283614183 1
 
0.1%
1.195009648 1
 
0.1%
1.085618271 1
 
0.1%
1.245496205 1
 
0.1%
1.100557673 1
 
0.1%
1.02512063 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.1014964018 1
0.1%
0.1796227233 1
0.1%
0.2305087229 1
0.1%
0.2389526753 1
0.1%
0.2518917234 1
0.1%
0.252605619 1
0.1%
0.2656711154 1
0.1%
0.2738333656 1
0.1%
0.2759850812 1
0.1%
0.2785597736 1
0.1%
ValueCountFrequency (%)
1.81765145 1
0.1%
1.815153158 1
0.1%
1.781565706 1
0.1%
1.750064255 1
0.1%
1.73681281 1
0.1%
1.731686279 1
0.1%
1.708982969 1
0.1%
1.704882956 1
0.1%
1.703995246 1
0.1%
1.702876312 1
0.1%

feat09
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98300027
Minimum0.14762559
Maximum1.9575205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:50.065557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.14762559
5-th percentile0.46215155
Q10.72939323
median0.98232479
Q31.2388684
95-th percentile1.4986618
Maximum1.9575205
Range1.8098949
Interquartile range (IQR)0.50947516

Descriptive statistics

Standard deviation0.32329693
Coefficient of variation (CV)0.32888794
Kurtosis-0.74965397
Mean0.98300027
Median Absolute Deviation (MAD)0.25442091
Skewness0.0038846067
Sum1966.0005
Variance0.1045209
MonotonicityNot monotonic
2024-01-16T22:56:50.127067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7804830712 1
 
0.1%
0.6750215028 1
 
0.1%
0.6437708266 1
 
0.1%
1.369157003 1
 
0.1%
1.102114554 1
 
0.1%
0.994823918 1
 
0.1%
1.020888845 1
 
0.1%
1.362078951 1
 
0.1%
0.6630857657 1
 
0.1%
0.495098946 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0.1476255899 1
0.1%
0.1701565356 1
0.1%
0.1793598737 1
0.1%
0.1841430741 1
0.1%
0.1958970806 1
0.1%
0.1980614381 1
0.1%
0.2274805217 1
0.1%
0.2276228134 1
0.1%
0.255110882 1
0.1%
0.2606577808 1
0.1%
ValueCountFrequency (%)
1.957520539 1
0.1%
1.77860816 1
0.1%
1.72978283 1
0.1%
1.721810834 1
0.1%
1.718660003 1
0.1%
1.718353369 1
0.1%
1.714843135 1
0.1%
1.714066814 1
0.1%
1.703434196 1
0.1%
1.6958625 1
0.1%

feat10
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5054663
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-01-16T22:56:50.190066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26138515
Q10.40767553
median0.50575331
Q30.60072289
95-th percentile0.73849414
Maximum1
Range1
Interquartile range (IQR)0.19304737

Descriptive statistics

Standard deviation0.14513357
Coefficient of variation (CV)0.28712808
Kurtosis0.1447056
Mean0.5054663
Median Absolute Deviation (MAD)0.096758469
Skewness0.011502807
Sum1010.9326
Variance0.021063753
MonotonicityNot monotonic
2024-01-16T22:56:50.252238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.483015696 1
 
0.1%
0.2563189799 1
 
0.1%
0.6063909164 1
 
0.1%
0.4509566908 1
 
0.1%
0.8044699298 1
 
0.1%
0.7525056404 1
 
0.1%
0.3776318794 1
 
0.1%
0.6526625646 1
 
0.1%
0.4405957064 1
 
0.1%
0.5516968308 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
0 1
0.1%
0.01755914126 1
0.1%
0.02869647038 1
0.1%
0.08773995037 1
0.1%
0.09091151016 1
0.1%
0.09255494225 1
0.1%
0.1162187482 1
0.1%
0.1336508328 1
0.1%
0.1347043418 1
0.1%
0.1347076462 1
0.1%
ValueCountFrequency (%)
1 1
0.1%
0.9621882888 1
0.1%
0.9562910225 1
0.1%
0.9486153634 1
0.1%
0.9485468191 1
0.1%
0.9291979016 1
0.1%
0.9234644965 1
0.1%
0.9228921468 1
0.1%
0.917108482 1
0.1%
0.9136572644 1
0.1%

foreign_worker
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
True
1924 
False
 
76
ValueCountFrequency (%)
True 1924
96.2%
False 76
 
3.8%
2024-01-16T22:56:50.304238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

housing
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
own
1444 
rent
346 
'for free'
210 

Length

Max length10
Median length3
Mean length3.908
Min length3

Characters and Unicode

Total characters7816
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowown
2nd rowown
3rd rowown
4th rowown
5th rowown

Common Values

ValueCountFrequency (%)
own 1444
72.2%
rent 346
 
17.3%
'for free' 210
 
10.5%

Length

2024-01-16T22:56:50.355018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.403018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
own 1444
65.3%
rent 346
 
15.7%
for 210
 
9.5%
free 210
 
9.5%

Most occurring characters

ValueCountFrequency (%)
n 1790
22.9%
o 1654
21.2%
w 1444
18.5%
r 766
9.8%
e 766
9.8%
' 420
 
5.4%
f 420
 
5.4%
t 346
 
4.4%
210
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7186
91.9%
Other Punctuation 420
 
5.4%
Space Separator 210
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1790
24.9%
o 1654
23.0%
w 1444
20.1%
r 766
10.7%
e 766
10.7%
f 420
 
5.8%
t 346
 
4.8%
Other Punctuation
ValueCountFrequency (%)
' 420
100.0%
Space Separator
ValueCountFrequency (%)
210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7186
91.9%
Common 630
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1790
24.9%
o 1654
23.0%
w 1444
20.1%
r 766
10.7%
e 766
10.7%
f 420
 
5.8%
t 346
 
4.8%
Common
ValueCountFrequency (%)
' 420
66.7%
210
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1790
22.9%
o 1654
21.2%
w 1444
18.5%
r 766
9.8%
e 766
9.8%
' 420
 
5.4%
f 420
 
5.4%
t 346
 
4.4%
210
 
2.7%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
4
978 
2
455 
3
304 
1
263 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

Length

2024-01-16T22:56:50.454032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.499035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

Most occurring characters

ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 978
48.9%
2 455
22.8%
3 304
 
15.2%
1 263
 
13.2%

job
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
skilled
1258 
'unskilled resident'
411 
'high qualif/self emp/mgmt'
291 
'unemp/unskilled non res'
 
40

Length

Max length27
Median length7
Mean length12.9415
Min length7

Characters and Unicode

Total characters25883
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowskilled
2nd rowskilled
3rd row'unskilled resident'
4th rowskilled
5th rowskilled

Common Values

ValueCountFrequency (%)
skilled 1258
62.9%
'unskilled resident' 411
 
20.5%
'high qualif/self emp/mgmt' 291
 
14.5%
'unemp/unskilled non res' 40
 
2.0%

Length

2024-01-16T22:56:50.560149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.612944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
skilled 1258
40.9%
unskilled 411
 
13.4%
resident 411
 
13.4%
high 291
 
9.5%
qualif/self 291
 
9.5%
emp/mgmt 291
 
9.5%
unemp/unskilled 40
 
1.3%
non 40
 
1.3%
res 40
 
1.3%

Most occurring characters

ValueCountFrequency (%)
l 4000
15.5%
e 3193
12.3%
i 2702
10.4%
s 2451
9.5%
d 2120
8.2%
k 1709
 
6.6%
' 1484
 
5.7%
1073
 
4.1%
n 982
 
3.8%
m 913
 
3.5%
Other values (11) 5256
20.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22704
87.7%
Other Punctuation 2106
 
8.1%
Space Separator 1073
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 4000
17.6%
e 3193
14.1%
i 2702
11.9%
s 2451
10.8%
d 2120
9.3%
k 1709
7.5%
n 982
 
4.3%
m 913
 
4.0%
u 782
 
3.4%
t 702
 
3.1%
Other values (8) 3150
13.9%
Other Punctuation
ValueCountFrequency (%)
' 1484
70.5%
/ 622
29.5%
Space Separator
ValueCountFrequency (%)
1073
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22704
87.7%
Common 3179
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 4000
17.6%
e 3193
14.1%
i 2702
11.9%
s 2451
10.8%
d 2120
9.3%
k 1709
7.5%
n 982
 
4.3%
m 913
 
4.0%
u 782
 
3.4%
t 702
 
3.1%
Other values (8) 3150
13.9%
Common
ValueCountFrequency (%)
' 1484
46.7%
1073
33.8%
/ 622
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 4000
15.5%
e 3193
12.3%
i 2702
10.4%
s 2451
9.5%
d 2120
8.2%
k 1709
 
6.6%
' 1484
 
5.7%
1073
 
4.1%
n 982
 
3.8%
m 913
 
3.5%
Other values (11) 5256
20.3%

num_dependents
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1694 
2
306 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

Length

2024-01-16T22:56:50.665103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.707103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

Most occurring characters

ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1694
84.7%
2 306
 
15.3%

other_parties
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
none
1818 
guarantor
 
104
'co applicant'
 
78

Length

Max length14
Median length4
Mean length4.65
Min length4

Characters and Unicode

Total characters9300
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 1818
90.9%
guarantor 104
 
5.2%
'co applicant' 78
 
3.9%

Length

2024-01-16T22:56:50.756234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.801235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
none 1818
87.5%
guarantor 104
 
5.0%
co 78
 
3.8%
applicant 78
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 3818
41.1%
o 2000
21.5%
e 1818
19.5%
a 364
 
3.9%
r 208
 
2.2%
t 182
 
2.0%
' 156
 
1.7%
c 156
 
1.7%
p 156
 
1.7%
g 104
 
1.1%
Other values (4) 338
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9066
97.5%
Other Punctuation 156
 
1.7%
Space Separator 78
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3818
42.1%
o 2000
22.1%
e 1818
20.1%
a 364
 
4.0%
r 208
 
2.3%
t 182
 
2.0%
c 156
 
1.7%
p 156
 
1.7%
g 104
 
1.1%
u 104
 
1.1%
Other values (2) 156
 
1.7%
Other Punctuation
ValueCountFrequency (%)
' 156
100.0%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9066
97.5%
Common 234
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3818
42.1%
o 2000
22.1%
e 1818
20.1%
a 364
 
4.0%
r 208
 
2.3%
t 182
 
2.0%
c 156
 
1.7%
p 156
 
1.7%
g 104
 
1.1%
u 104
 
1.1%
Other values (2) 156
 
1.7%
Common
ValueCountFrequency (%)
' 156
66.7%
78
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3818
41.1%
o 2000
21.5%
e 1818
19.5%
a 364
 
3.9%
r 208
 
2.2%
t 182
 
2.0%
' 156
 
1.7%
c 156
 
1.7%
p 156
 
1.7%
g 104
 
1.1%
Other values (4) 338
 
3.6%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
none
1634 
bank
267 
stores
 
99

Length

Max length6
Median length4
Mean length4.099
Min length4

Characters and Unicode

Total characters8198
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 1634
81.7%
bank 267
 
13.4%
stores 99
 
5.0%

Length

2024-01-16T22:56:50.853747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.904747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
none 1634
81.7%
bank 267
 
13.4%
stores 99
 
5.0%

Most occurring characters

ValueCountFrequency (%)
n 3535
43.1%
o 1733
21.1%
e 1733
21.1%
b 267
 
3.3%
a 267
 
3.3%
k 267
 
3.3%
s 198
 
2.4%
t 99
 
1.2%
r 99
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8198
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 3535
43.1%
o 1733
21.1%
e 1733
21.1%
b 267
 
3.3%
a 267
 
3.3%
k 267
 
3.3%
s 198
 
2.4%
t 99
 
1.2%
r 99
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8198
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 3535
43.1%
o 1733
21.1%
e 1733
21.1%
b 267
 
3.3%
a 267
 
3.3%
k 267
 
3.3%
s 198
 
2.4%
t 99
 
1.2%
r 99
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 3535
43.1%
o 1733
21.1%
e 1733
21.1%
b 267
 
3.3%
a 267
 
3.3%
k 267
 
3.3%
s 198
 
2.4%
t 99
 
1.2%
r 99
 
1.2%

own_telephone
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
none
1174 
yes
826 

Length

Max length4
Median length4
Mean length3.587
Min length3

Characters and Unicode

Total characters7174
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rownone
3rd rownone
4th rownone
5th rowyes

Common Values

ValueCountFrequency (%)
none 1174
58.7%
yes 826
41.3%

Length

2024-01-16T22:56:50.954259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:50.999259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
none 1174
58.7%
yes 826
41.3%

Most occurring characters

ValueCountFrequency (%)
n 2348
32.7%
e 2000
27.9%
o 1174
16.4%
y 826
 
11.5%
s 826
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7174
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2348
32.7%
e 2000
27.9%
o 1174
16.4%
y 826
 
11.5%
s 826
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 7174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2348
32.7%
e 2000
27.9%
o 1174
16.4%
y 826
 
11.5%
s 826
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2348
32.7%
e 2000
27.9%
o 1174
16.4%
y 826
 
11.5%
s 826
 
11.5%

personal_status
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
'male single'
1065 
'female div/dep/mar'
649 
'male mar/wid'
181 
'male div/sep'
 
105

Length

Max length20
Median length13
Mean length15.4145
Min length13

Characters and Unicode

Total characters30829
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'male single'
2nd row'female div/dep/mar'
3rd row'male single'
4th row'female div/dep/mar'
5th row'female div/dep/mar'

Common Values

ValueCountFrequency (%)
'male single' 1065
53.2%
'female div/dep/mar' 649
32.5%
'male mar/wid' 181
 
9.0%
'male div/sep' 105
 
5.2%

Length

2024-01-16T22:56:51.050288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:51.101109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
male 1351
33.8%
single 1065
26.6%
female 649
16.2%
div/dep/mar 649
16.2%
mar/wid 181
 
4.5%
div/sep 105
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 4468
14.5%
' 4000
13.0%
l 3065
9.9%
a 2830
9.2%
m 2830
9.2%
2000
 
6.5%
i 2000
 
6.5%
d 1584
 
5.1%
/ 1584
 
5.1%
s 1170
 
3.8%
Other values (7) 5298
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23245
75.4%
Other Punctuation 5584
 
18.1%
Space Separator 2000
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4468
19.2%
l 3065
13.2%
a 2830
12.2%
m 2830
12.2%
i 2000
8.6%
d 1584
 
6.8%
s 1170
 
5.0%
n 1065
 
4.6%
g 1065
 
4.6%
r 830
 
3.6%
Other values (4) 2338
10.1%
Other Punctuation
ValueCountFrequency (%)
' 4000
71.6%
/ 1584
 
28.4%
Space Separator
ValueCountFrequency (%)
2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23245
75.4%
Common 7584
 
24.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4468
19.2%
l 3065
13.2%
a 2830
12.2%
m 2830
12.2%
i 2000
8.6%
d 1584
 
6.8%
s 1170
 
5.0%
n 1065
 
4.6%
g 1065
 
4.6%
r 830
 
3.6%
Other values (4) 2338
10.1%
Common
ValueCountFrequency (%)
' 4000
52.7%
2000
26.4%
/ 1584
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4468
14.5%
' 4000
13.0%
l 3065
9.9%
a 2830
9.2%
m 2830
9.2%
2000
 
6.5%
i 2000
 
6.5%
d 1584
 
5.1%
/ 1584
 
5.1%
s 1170
 
3.8%
Other values (7) 5298
17.2%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
car
668 
'real estate'
561 
'life insurance'
462 
'no known property'
309 

Length

Max length19
Median length16
Mean length11.28
Min length3

Characters and Unicode

Total characters22560
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'real estate'
2nd row'life insurance'
3rd row'real estate'
4th row'real estate'
5th row'real estate'

Common Values

ValueCountFrequency (%)
car 668
33.4%
'real estate' 561
28.1%
'life insurance' 462
23.1%
'no known property' 309
15.4%

Length

2024-01-16T22:56:51.157554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:51.208552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
car 668
18.3%
real 561
15.4%
estate 561
15.4%
life 462
12.7%
insurance 462
12.7%
no 309
8.5%
known 309
8.5%
property 309
8.5%

Most occurring characters

ValueCountFrequency (%)
e 2916
12.9%
' 2664
11.8%
r 2309
10.2%
a 2252
10.0%
n 1851
8.2%
1641
 
7.3%
t 1431
 
6.3%
c 1130
 
5.0%
l 1023
 
4.5%
s 1023
 
4.5%
Other values (8) 4320
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18255
80.9%
Other Punctuation 2664
 
11.8%
Space Separator 1641
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2916
16.0%
r 2309
12.6%
a 2252
12.3%
n 1851
10.1%
t 1431
7.8%
c 1130
 
6.2%
l 1023
 
5.6%
s 1023
 
5.6%
o 927
 
5.1%
i 924
 
5.1%
Other values (6) 2469
13.5%
Other Punctuation
ValueCountFrequency (%)
' 2664
100.0%
Space Separator
ValueCountFrequency (%)
1641
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18255
80.9%
Common 4305
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2916
16.0%
r 2309
12.6%
a 2252
12.3%
n 1851
10.1%
t 1431
7.8%
c 1130
 
6.2%
l 1023
 
5.6%
s 1023
 
5.6%
o 927
 
5.1%
i 924
 
5.1%
Other values (6) 2469
13.5%
Common
ValueCountFrequency (%)
' 2664
61.9%
1641
38.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2916
12.9%
' 2664
11.8%
r 2309
10.2%
a 2252
10.0%
n 1851
8.2%
1641
 
7.3%
t 1431
 
6.3%
c 1130
 
5.0%
l 1023
 
4.5%
s 1023
 
4.5%
Other values (8) 4320
19.1%

purpose
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
radio/tv
563 
'new car'
466 
furniture/equipment
356 
business
211 
'used car'
183 
Other values (5)
221 

Length

Max length20
Median length19
Mean length10.5635
Min length5

Characters and Unicode

Total characters21127
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfurniture/equipment
2nd rowfurniture/equipment
3rd row'new car'
4th rowradio/tv
5th rowradio/tv

Common Values

ValueCountFrequency (%)
radio/tv 563
28.1%
'new car' 466
23.3%
furniture/equipment 356
17.8%
business 211
 
10.5%
'used car' 183
 
9.2%
education 95
 
4.8%
repairs 51
 
2.5%
'domestic appliance' 31
 
1.6%
other 25
 
1.2%
retraining 19
 
0.9%

Length

2024-01-16T22:56:51.270065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:51.328578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
car 649
24.2%
radio/tv 563
21.0%
new 466
17.4%
furniture/equipment 356
13.3%
business 211
 
7.9%
used 183
 
6.8%
education 95
 
3.5%
repairs 51
 
1.9%
domestic 31
 
1.2%
appliance 31
 
1.2%
Other values (2) 44
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 2180
 
10.3%
r 2089
 
9.9%
i 1732
 
8.2%
u 1557
 
7.4%
n 1553
 
7.4%
t 1445
 
6.8%
a 1439
 
6.8%
' 1360
 
6.4%
/ 919
 
4.3%
s 898
 
4.3%
Other values (14) 5955
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18168
86.0%
Other Punctuation 2279
 
10.8%
Space Separator 680
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2180
12.0%
r 2089
11.5%
i 1732
9.5%
u 1557
8.6%
n 1553
8.5%
t 1445
8.0%
a 1439
7.9%
s 898
 
4.9%
d 872
 
4.8%
c 806
 
4.4%
Other values (11) 3597
19.8%
Other Punctuation
ValueCountFrequency (%)
' 1360
59.7%
/ 919
40.3%
Space Separator
ValueCountFrequency (%)
680
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18168
86.0%
Common 2959
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2180
12.0%
r 2089
11.5%
i 1732
9.5%
u 1557
8.6%
n 1553
8.5%
t 1445
8.0%
a 1439
7.9%
s 898
 
4.9%
d 872
 
4.8%
c 806
 
4.4%
Other values (11) 3597
19.8%
Common
ValueCountFrequency (%)
' 1360
46.0%
/ 919
31.1%
680
23.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2180
 
10.3%
r 2089
 
9.9%
i 1732
 
8.2%
u 1557
 
7.4%
n 1553
 
7.4%
t 1445
 
6.8%
a 1439
 
6.8%
' 1360
 
6.4%
/ 919
 
4.3%
s 898
 
4.3%
Other values (14) 5955
28.2%

residence_since
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
4
818 
2
617 
3
289 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

Length

2024-01-16T22:56:51.399577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:51.449007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

Most occurring characters

ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 818
40.9%
2 617
30.9%
3 289
 
14.4%
1 276
 
13.8%

savings_status
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
'<100'
1213 
'no known savings'
357 
'100<=X<500'
203 
'500<=X<1000'
134 
'>=1000'
 
93

Length

Max length18
Median length6
Mean length9.313
Min length6

Characters and Unicode

Total characters18626
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'<100'
2nd row'>=1000'
3rd row'<100'
4th row'<100'
5th row'100<=X<500'

Common Values

ValueCountFrequency (%)
'<100' 1213
60.7%
'no known savings' 357
 
17.8%
'100<=X<500' 203
 
10.2%
'500<=X<1000' 134
 
6.7%
'>=1000' 93
 
4.7%

Length

2024-01-16T22:56:51.512014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-16T22:56:51.565522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100 1213
44.7%
no 357
 
13.2%
known 357
 
13.2%
savings 357
 
13.2%
100<=x<500 203
 
7.5%
500<=x<1000 134
 
4.9%
1000 93
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 4187
22.5%
' 4000
21.5%
< 1887
10.1%
1 1643
 
8.8%
n 1428
 
7.7%
714
 
3.8%
s 714
 
3.8%
o 714
 
3.8%
= 430
 
2.3%
k 357
 
1.9%
Other values (8) 2552
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6167
33.1%
Lowercase Letter 4998
26.8%
Other Punctuation 4000
21.5%
Math Symbol 2410
 
12.9%
Space Separator 714
 
3.8%
Uppercase Letter 337
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1428
28.6%
s 714
14.3%
o 714
14.3%
k 357
 
7.1%
w 357
 
7.1%
a 357
 
7.1%
v 357
 
7.1%
i 357
 
7.1%
g 357
 
7.1%
Decimal Number
ValueCountFrequency (%)
0 4187
67.9%
1 1643
 
26.6%
5 337
 
5.5%
Math Symbol
ValueCountFrequency (%)
< 1887
78.3%
= 430
 
17.8%
> 93
 
3.9%
Other Punctuation
ValueCountFrequency (%)
' 4000
100.0%
Space Separator
ValueCountFrequency (%)
714
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 337
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13291
71.4%
Latin 5335
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1428
26.8%
s 714
13.4%
o 714
13.4%
k 357
 
6.7%
w 357
 
6.7%
a 357
 
6.7%
v 357
 
6.7%
i 357
 
6.7%
g 357
 
6.7%
X 337
 
6.3%
Common
ValueCountFrequency (%)
0 4187
31.5%
' 4000
30.1%
< 1887
14.2%
1 1643
 
12.4%
714
 
5.4%
= 430
 
3.2%
5 337
 
2.5%
> 93
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4187
22.5%
' 4000
21.5%
< 1887
10.1%
1 1643
 
8.8%
n 1428
 
7.7%
714
 
3.8%
s 714
 
3.8%
o 714
 
3.8%
= 430
 
2.3%
k 357
 
1.9%
Other values (8) 2552
13.7%

Interactions

2024-01-16T22:56:46.833037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.730066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.437020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.082759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.759369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.548622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.174134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.806024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.468148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.135592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.969648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.640282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.308729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.994923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.881032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.780427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.487021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.139269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.808373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.595623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.221136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.854949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.517150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.329854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.026167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.691279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.357241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.050435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.923039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.824939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.528532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.186269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.855883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.637130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.266642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.897949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.563216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.372856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.072165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.739795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.405257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.098434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.969545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.873939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.574529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.232779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.905939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.685132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.314645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.946464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.612221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.422862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.124175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.793795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.464300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.149598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.018549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.922447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.621536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.281779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.953969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.730330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.360471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.992465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.658262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.474366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.169691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.844300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.517304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.196602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.065060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:37.972451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.672659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.327295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.999969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.773355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.401473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.040975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.702657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.525878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.214694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.889687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.566365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.243145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.110057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.020458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.715662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.374292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.044994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.815099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.444984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.086974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.748169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.572876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.261198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.934199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.615366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.288142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.162574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.071969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.762170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.426840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.092994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.859608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.486984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.134582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.796171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.625946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.308199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.978199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.660528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.339655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.212577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.122475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.805171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.474840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.140507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.903606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.531494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.182101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.844460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.675946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.355709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.025709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.707527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.389657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.258687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.178480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.849926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.523346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.325088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.949115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.579494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.229611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.890461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.724454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.405709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.072708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.757048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.446170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.306689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.235995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.893739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.571349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.371088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.992115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.624006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.276612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.937510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.771457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.453250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.120716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.805051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.647199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.352199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.290995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.942251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.620352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.415091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.038625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.670006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.321615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.985571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.817459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.501249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.169218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.851572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.694200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.398201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.339509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.990248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.669861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.459115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.084626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.715009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.370123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.038081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.865970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.548756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.215226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.898571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.739708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:47.444551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:38.391508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.038759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:39.715862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:40.506113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.132133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:41.763025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:42.417126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.090081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:43.920133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:44.594757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.262729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:45.949090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-16T22:56:46.788711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-01-16T22:56:47.530062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-16T22:56:47.705551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idagechecking_statusclasscredit_amountcredit_historydurationemploymentexisting_creditsfeat01feat02feat03feat04feat05feat06feat07feat08feat09feat10foreign_workerhousinginstallment_commitmentjobnum_dependentsother_partiesother_payment_plansown_telephonepersonal_statusproperty_magnitudepurposeresidence_sincesavings_status
0150'no checking'good2319'existing paid'21'>=7'20.2398980.6142301.3096590.9764460.7099720.5697100.9508350.5814580.7804830.483016yesown4skilled1nonenonenone'male single''real estate'furniture/equipment2'<100'
1230'no checking'good1163'delayed previously'15'4<=X<7'20.5211390.7904721.4527371.2002291.2028830.9574880.5149711.1799691.1380190.327573yesown3skilled1nonenonenone'female div/dep/mar''life insurance'furniture/equipment2'>=1000'
2332'no checking'good1502'critical/other existing credit'10'>=7'20.2868380.5417560.9573400.8559040.5442961.1403191.0389061.4972931.1305360.481874noown3'unskilled resident'2nonenonenone'male single''real estate''new car'4'<100'
3434'no checking'good4436'delayed previously'36'1<=X<4'20.4309370.5311400.9418301.1636471.1704901.3391950.9793200.5320721.0846280.630284yesown4skilled1nonenonenone'female div/dep/mar''real estate'radio/tv4'<100'
4521'no checking'good10155'existing paid'60'4<=X<7'10.4396430.6026671.1085560.7637941.0253250.6335430.8455101.0399551.2561990.518012yesown2skilled1nonenoneyes'female div/dep/mar''real estate'radio/tv4'100<=X<500'
5625'no checking'good3413'existing paid'24'<1'20.4296840.5865260.6337631.0358220.6602740.7922280.9518951.3346390.7688940.401252yesown4skilled1nonenonenone'male single'carradio/tv2'no known savings'
6736'0<=X<200'good2235'existing paid'24'>=7'10.3386250.5843410.6915841.1989051.4302050.7164091.1102041.5471221.4094250.632783yes'for free'4skilled1nonebankyes'male single''no known property''used car'4'no known savings'
7825'<0'good3555'existing paid'18'4<=X<7'10.6338440.4938611.1618811.3967791.5081450.4390020.5441520.5797321.1519940.407196yesown4skilled1guarantornonenone'female div/dep/mar''real estate'radio/tv1'<100'
8921'no checking'good1805'existing paid'6'1<=X<4'10.6017770.4676861.1093131.2428051.2065970.9758471.2551881.5276511.3524360.610364yesrent1skilled1nonenonenone'male mar/wid''life insurance'furniture/equipment2'<100'
91036'0<=X<200'good2756'critical/other existing credit'36'>=7'10.5525750.5693471.1294180.3571051.5477721.4042490.8431651.3171880.9676420.487436yesown4skilled1nonenonenone'male single''real estate'radio/tv4'<100'
idagechecking_statusclasscredit_amountcredit_historydurationemploymentexisting_creditsfeat01feat02feat03feat04feat05feat06feat07feat08feat09feat10foreign_workerhousinginstallment_commitmentjobnum_dependentsother_partiesother_payment_plansown_telephonepersonal_statusproperty_magnitudepurposeresidence_sincesavings_status
1990199137'no checking'good2987'critical/other existing credit'4'4<=X<7'10.6030800.5352830.8715071.3191050.2497681.4186240.6985110.9776540.9790410.445986yesown1skilled2nonenonenone'female div/dep/mar''real estate''new car'1'<100'
1991199238'no checking'good1041'critical/other existing credit'12unemployed10.3300750.5488860.6079441.0737280.6677391.6045600.5667471.1283150.9407730.556918yesown1'unemp/unskilled non res'1nonenonenone'female div/dep/mar''life insurance''new car'2'<100'
1992199336'no checking'good3143'existing paid'36'1<=X<4'10.7807440.6169691.0834610.6661491.2577621.5253780.4569491.3058490.4888960.574030yesown4skilled1nonenonenone'male single''real estate''new car'4'no known savings'
1993199432'0<=X<200'good6369'existing paid'12'4<=X<7'10.4802460.7927330.6041151.2930100.9435120.9771090.8468101.3275570.4714080.540818yesown2skilled1nonenonenone'male single'car'new car'2'<100'
1994199534'0<=X<200'good3070'critical/other existing credit'24'4<=X<7'20.2259140.5934081.0412951.1404281.0276780.8514800.8269041.2118140.8993650.652175yesown4skilled2nonenoneyes'male single''no known property'business3'no known savings'
1995199630'0<=X<200'good1743'existing paid'6'<1'10.3532280.4623411.2204121.2933621.2301400.9667040.7551071.1345050.8029610.587993yesrent4'high qualif/self emp/mgmt'1nonenoneyes'male mar/wid'carradio/tv3'<100'
1996199724'<0'bad2994'existing paid'18'1<=X<4'10.6446270.5436701.0424810.8436881.2769100.7014361.0547151.3549131.2083140.728793yesown2skilled1nonenonenone'female div/dep/mar''real estate'radio/tv2'<100'
1997199826'no checking'good1255'existing paid'12'1<=X<4'10.5977820.5906751.2272550.9582951.3522601.3665991.0036741.1310581.2375680.542453noown2skilled1nonenoneyes'male mar/wid''real estate'business2'<100'
1998199939'no checking'good1637'existing paid'12'>=7'10.4626090.5191580.6204290.5746311.2155880.8179830.6403421.1838380.7530620.281654yesown4'high qualif/self emp/mgmt'1nonenoneyes'male single'car'new car'4'<100'
1999200055'no checking'good1439'existing paid'12'4<=X<7'10.5067940.4702180.8505470.7952680.9514190.7493320.7350161.1205621.3737230.615641noown3skilled1nonenonenone'male single''life insurance''used car'2'>=1000'